Aviz Networks Blogs

Modern AI infrastructure no longer only means networking together a stack of GPUs with a bunch of switches. The modern AI factory now relies on tightly optimized networking in which a slight configuration miss will affect performance, price or deployment timeline. Traditionally, this means it requires setting up and testing the network configuration in a physical lab which is expensive, time consuming and not easily scalable. Why, you ask? Because hardware testing alone can be cost prohibitive. Design cycles are serial and iterative, meaning designs and validation are split into distinct phases that take weeks or days to cycle through. When you're designing for the future, you have configuration misses which aren’t caught until you’ve rolled out production. It’s time to disrupt the cycle with digital twins. What NVIDIA AIR and Aviz ONES give you NVIDIA AIR provides an all-digital, virtualized AI network simulation where you can simulate all the switches, links, automation and confi...

Many hackers are able to breach various parts of the healthcare system because the SecOps team is unable to quickly understand how far an attacker can spread throughout multiple systems. Hospitals, clinics, telemedicine, cloud services, devices and facility systems create traffic on a common network; however, due to lack of central control over how traffic flows & devices in the Silverado system, many of these facilities have blind spots pertaining to the analysis of East to West traffic, devices that are not monitored (unmanaged) & mixed infrastructure (devices that do not have agent-based security monitoring). Some examples of this are: Almost all types of ransomware operated by transferring themselves around to other locations before they perform file&folder encryptions. A lot of the different device types and workloads out there within the Healthcare ecosystem will not be able to utilize endpoint agents. Packet level evidence will assist with identifying unusual flows o...

In today's world of healthcare, thousands of connected devices rely on one another, such as infusion pumps, imaging equipment, badge readers, controllers of H/VAC, clinical stations and tele-health endpoints. A majority of these types of devices do not support security agents, can't be patched and were not designed with cybersecurity in mind; they were simply designed to provide safe reliable clinical care.  Here are some real-world observations related to the above statement: • There are substantial amounts of valuable traffic signals that are generated on connected medial and facility devices • Many of the devices cannot be secured by the use of traditional endpoint controls • Packet-level visibility can provide insight into device behaviour patterns, communications, and risk • Optimized delivery of traffic helps improve the accuracy of asset discovery and monitoring tools • Network evidence can support security compliance and incident investigation processes There is a comm...

AI factories have already introduced a completely new way of thinking regarding networking. Network used to serve the purpose of connecting devices. Nowadays, it influences the performance of workload execution, the level of isolation, usage of GPU, troubleshooting speeds, etc. It has become possible to validate the operational processes by creating a digital twin environment before any changes get implemented. By doing that, it would be possible to test AI fabrics, topology configuration options, create tenants, telemetry gathering, and much more within the environment. It is important not to forget about switching from automated deployment and management to intelligent operations. It will be possible to analyze alarms, find reasons for network congestion, detect drifts in configuration, examine telemetry information, generate health summaries, and so on. In other words, there won't be such a need to use multiple dashboards and CLI configurations. To sum up, an AI-friendly network...

PCI-DSS 4.0 raises the bar for financial services organisations by making compliance more evidence-driven. Institutions now need to show ongoing proof for encryption in transit, certificate validity, user activity, network monitoring, insecure protocols, malware signals, and risk analysis across complex environments. This is difficult because financial traffic no longer stays inside one controlled data center. It moves across hybrid cloud, APIs, branches, partners, payment systems, and internal workloads. Application logs and endpoint telemetry are useful, but they often depend on the health and honesty of the system generating them. During failures or attacks, that evidence can become incomplete. Packet-derived evidence helps close this visibility gap by observing traffic independently at the network layer. It can show which protocols are being used, which TLS sessions are active, what certificates are present, which DNS queries are occurring, which web transactions are visible, and ...

AI factories have already introduced a completely new way of thinking regarding networking. Network used to serve the purpose of connecting devices. Nowadays, it influences the performance of workload execution, the level of isolation, usage of GPU, troubleshooting speeds, etc. It has become possible to validate the operational processes by creating a digital twin environment before any changes get implemented. By doing that, it would be possible to test AI fabrics, topology configuration options, create tenants, telemetry gathering, and much more within the environment. It is important not to forget about switching from automated deployment and management to intelligent operations. It will be possible to analyze alarms, find reasons for network congestion, detect drifts in configuration, examine telemetry information, generate health summaries, and so on. In other words, there won't be such a need to use multiple dashboards and CLI configurations. To sum up, an AI-friendly network...